使用健康信息交换数据识别有自杀风险的患者
Shane J Sacco1,2,3, Kun Chen1,2, Jun Jin1,2
1Department of Statistics, University of Connecticut, Storrs, CT, US.
BMC public health
|April 29, 2025
概括
使用健康信息交换 (HIE) 数据预测自杀风险是有前途的. 从各种HIE数据开发的模型取得了强的表现,突出了这种方法在识别有自杀企图风险的个人的潜力.
科学领域:
- 公共卫生 公共卫生
- 医疗信息学 医疗信息学
- 生物统计学 生物统计学
背景情况:
- 自杀是美国的主要死亡原因,在过去二十年中,自杀率显著增加.
- 在医疗保健机构准确识别自杀风险是国家优先事项.
- 使用健康记录的统计模型显示了预测自杀风险的潜力.
研究的目的:
- 评估自杀企图预测模型的可行性和性能,使用来自健康信息交换 (HIE) 的数据.
- 确定涵盖各种医疗保健环境的HIE数据是否可以改善自杀风险预测.
- 通过这些模型检查那些被确定为高风险患者的特征.
主要方法:
- 用2012-2017年HIE数据对18-64岁的堪萨斯州居民进行了回顾性队列研究.
- 使用患者人口统计和医学诊断代码开发自杀企图的预测模型.
- 使用ROC曲线下的面积 (AUROC) 评估模型性能,并分析预测因素的重要性.
主要成果:
- 这项研究包括501,595名个人,自杀企图率为0.4%.
- 最好的预测模型实现了0.82的AUROC,与现有模型相比.
- 预测者包括诊断代码,反映了通过HIE获得的各种临床数据.
结论:
- 来自健康信息交换的真实世界数据为预测自杀行为提供了显著的潜力.
- 在HIEs数据的多样性可能为强大的自杀风险建模提供了前所未有的机会.
- 可以利用HIE数据开发有效的自杀风险预测工具.
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